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Brian Le Vu profile illustration

Hi, I'm Brian Le Vu,

Builder-operator
who builds systems from scratch.

15+ years building products, teams, and operating systems from zero to scale.

Product builderMarketplace operatorAI workflow designerTeam builder
15+years building from scratch
3chapters: entertainment, marketplaces, AI ops
10M+earners served by the platform

I have built from scratch in three very different arenas.
The pattern is the same: build systems that last.

Moosin

Turned offline entertainment into a digital business.

Head of Product for a shift into digital entertainment and social media.

Uber SEA

Built marketplace growth from the ground up.

Launched products, scaled regional operations, and built online acquisition.

Uber Product Ops

Built AI-native operating systems for global-scale marketplace work.

Designed processes, rituals, and team culture for a platform serving 10M+ earners globally.

My path
What I buildLinkedInAltai VenturesContact

System Design

The work is not coordination. It is operating system design.

A product organization gets faster when market truth, team rituals, and AI workflows are designed as one system. That is the thread underneath the CV.

Interactive Strategy Console

Turn noisy marketplace signals into product choices.

1customer + market signals
2leadership-ready tradeoffs
3one recommendation
Workflow sketch
if signal.isNoisy():
  triangulate(field, data, support)
  ship(decision_memo)
Experience

Built from field operations into global marketplace leadership.

2024-Present

San Francisco Bay Area

Senior Product Operations Leader, Marketplace Experience

Uber

Leads Product Operations for marketplace experience workstreams, connecting customer reality, product strategy, launch readiness, and operating cadences across complex marketplace environments.

  • Builds and leads a high-performing Product Ops team across complex, cross-functional product areas.
  • Owns product operating rhythms that help leadership teams make faster, better-grounded decisions.
  • Drives team AI adoption through demos, workflow redesign, knowledge systems, and practical upskilling rituals.

2019-2024

Global

Marketplace Experience Product Operations Lead

Uber

Drove product operations across marketplace experience, launch readiness, customer feedback loops, program governance, and marketplace health initiatives.

  • Scaled launch discipline across global programs and region-specific product contexts.
  • Connected customer and market feedback loops to product prioritization and leadership reviews.
  • Built team operating models, onboarding systems, and product support workflows.

2015-2018

Singapore

Regional Operations, New Supply and Driver Access

Uber APAC / SENA

Led regional marketplace growth and access workstreams during Uber's hypergrowth phase, bridging city operations, product localization, analytics, CRM, and field research.

  • Worked across multiple mature and emerging Asia-Pacific markets.
  • Built repeatable approaches for acquisition, activation, conversion, and cross-market experimentation.
  • Developed the marketplace instincts that later anchored global product operations leadership.

2011-2015

Vietnam

Head of Product

Moosin

Helped transform an offline entertainment business into a digital entertainment and social media company.

  • Built the product foundation for a new digital business model.
  • Connected offline operations, content, distribution, and user growth.
  • Developed the early builder instincts that later carried into marketplace and AI operating systems.
Portfolio

Selected work themes, adapted for a public portfolio.

Filter

Team Building · Team system

High-Performing Product Ops Team

A Product Ops team model built around clear ownership, stronger product judgment, crisp escalation, and consistent operating rituals.

Supporting signals

Hiring systems, onboarding, calibration, team charters, demos, leadership cadences, and a clear bar for strategic Product Ops work.

AI · AI system

AI-Native Product Ops Adoption

A team upskilling program that moves Product Ops from basic AI usage toward agent-assisted research, reporting, product context retrieval, and decision support.

Supporting signals

Lunch-and-learns, sub-team demos, lighthouse workflows, reusable knowledge systems, and a 90-day plan to turn local market truth into global product leverage.

Product Leadership · Product system

Marketplace Product Operating Model

A leadership cadence that keeps product, operations, regional teams, support, policy, and data aligned around real customer and marketplace constraints.

Supporting signals

Business reviews, launch governance, team charters, stakeholder maps, and decision-ready leadership narratives.

Marketplace · Market system

Marketplace Sentiment System

A durable way to convert qualitative feedback, competitive benchmarking, survey signals, and marketplace performance into product strategy.

Supporting signals

Used to separate perception gaps from structural issues and to focus leadership attention on the problems that change trust.

Marketplace · Market system

Marketplace Transparency and Trust

Product operations around how marketplace participants understand value, reliability, incentives, fairness, and trust in complex product systems.

Supporting signals

Translated complex product systems into clear risks, launch requirements, and leadership tradeoffs.

Marketplace · Market system

Loyalty, Progression, and Quality Programs

Global operating support for programs that reward high-quality marketplace behavior while preserving clarity, fairness, and regional adaptability.

Supporting signals

Program architecture, rollout readiness, local-market constraints, and customer comprehension across marketplace contexts.

AI · AI system

AI-Native Product Ops Knowledge Base

A personal and team operating layer that keeps product truth, market context, meeting decisions, and codebase knowledge retrievable without relying on memory or stale docs.

Supporting signals

Obsidian-backed workflows, agentic research, source-backed briefings, and structured executive prep.

Operating Principles

How I tend to run the work.

Ground truth beats theater: start with customers, markets, and field reality before polishing the narrative.

One clear recommendation is more useful than five options with no owner.

The best Product Ops teams do not just coordinate product work; they raise the quality and speed of product decisions.

AI adoption is a team operating model problem before it is a tooling problem.

Product Leadership

Product operating modelsGroup-level prioritizationExecutive product reviewsDecision qualityCross-functional ownership

Marketplace Product Strategy

Marketplace healthCustomer trustSentiment systemsCompetitive benchmarkingIncentive and loyalty systems

Product Ops Craft

Global launch readinessExecutive reviewsProgram governanceCross-functional escalationOperating cadence design

Team Leadership

Cross-geo team managementAI upskillingHiring systemsPromotion narrativesStakeholder mappingAsync-first communication

AI-Native Systems

Knowledge basesAgent workflowsPrompted researchAutomation designDecision-support tools
FAQ

The short version for collaborators.

Where does Brian add the most leverage?

In product organizations where marketplace complexity, customer reality, team performance, AI workflow adoption, and leadership decision quality need to connect.

What is the through-line in Brian's experience?

The through-line is product leadership in complex marketplaces: building teams, understanding how marketplace participants make decisions, translating messy market signals into product strategy, and building systems that make large organizations respond faster.

What makes this profile relevant in the AI era?

Brian is not positioning as an AI engineer. He is positioning as a Product Ops leader who can redesign product workflows around AI: agents for context retrieval, source-backed product truth, better team rituals, and human-in-the-loop judgment for high-stakes marketplace decisions.

Why keep this public-safe instead of using all internal details?

A public portfolio should communicate scope, judgment, and craft without publishing confidential company details. This site intentionally avoids sensitive metrics, private stakeholder context, and internal-only program specifics.